Contamination estimation of formation samples

ABSTRACT

Contamination estimation of a mud filtrate or reservoir sample requires a robust handle on the properties of mud filtrate at downhole conditions. Coupling acquired data with downhole measured data provides a robust estimation of contamination by encompassing the entire available data. Downhole density of the mud filtrate sample may be estimated based on a characteristic of the mud filtrate sample. A density of a formation fluid of the reservoir may be determined using a formation tester tool. The contamination of the formation fluid may be estimated based on the clean fluid density and the estimated mud filtrate density by, for example, using a material balance equation or ratio. An estimated pump-out time for the formation fluid may be determined based on the estimated contamination and a trend of the estimated contamination of the formation fluid.

TECHNICAL FIELDS

The present disclosure relates generally to testing and evaluation ofsubterranean or subsea formation fluids or samples, and morespecifically (although not necessarily exclusively), to systems andmethods for improving the contamination estimation of samples using aformation tester.

BACKGROUND

To evaluate prospects of an underground hydrocarbon reservoir, arepresentative sample of the reservoir fluid may be captured fordetailed analysis. A sample of the reservoir fluid may be obtained bylowering a tool having a sampling chamber into the wellbore at apredetermined or sampling depth and fluid is allowed to flow into thesampling chamber. After the sample is collected, the tool may bewithdrawn from the wellbore so that the sample of reservoir fluid may beanalyzed.

Fluid analysis is possible using pump-out formation testers that providedownhole measurements of certain fluid properties and enable collectionof a large number of representative samples stored at downholeconditions. Generally, a wellbore is filled with a drilling fluid, forexample, a mud. The drilling fluid may be water-based or oil-based, isused as a lubricant and aids in the removal of cuttings from thewellbore. The drilling fluid also is used to maintain a pressure.Hydrocarbons contained in subterranean formations are usually at a highpressure. Standard overbalanced drilling techniques require that thehydrostatic pressure in the wellbore exceed the formation pressure,thereby preventing formation or reservoir fluids from flowinguncontrolled into the wellbore.

When the hydrostatic pressure of the drilling fluid is greater thanpressure of surrounding formation, a portion of the drilling fluid knowncommonly as the mud filtrate will tend to penetrate the surroundingformation. The fluid in the formation close to the wellbore will be amixture of this mud filtrate and the reservoir fluid or formation fluid.The presence of the mud filtrate in the reservoir fluid can interferewith attempts to sample and analyze the reservoir fluid. As a reservoirfluid sample is drawn from the formation at the wall of the wellbore,the first samples of reservoir fluid pumped may comprise primarily mudfiltrate, with the amount of mud filtrate in the mixture typicallydecreasing as pumped volume increases. To avoid collecting mud filtratein the collected sample, pumping is continued for a period of timebefore the collection of the fluid sample. Obtaining representativereservoir fluid samples with minimum rig time to determine accuratereservoir fluid properties and contamination while sampling with aformation tester may result in a reduction of overall costs andconservation of resources.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of one or more aspects of the presentinvention and advantages thereof may be acquired by referring to thefollowing description taken in conjunction with the accompanyingdrawings, in which like reference number indicate like features.

FIG. 1 is a schematic diagram of an apparatus for transferring orretrieving material in a wellbore, according to one or more aspects ofthe present disclosure.

FIG. 2 is a diagram illustrating an example information handling system,according to one or more aspects of the present disclosure.

FIG. 3 is a flowchart of a method of contamination estimation, accordingto one or more aspects of the present disclosure.

FIG. 4 is a plot illustrating density of formation fluid versus time,according to one or more aspects of the present disclosure.

FIG. 5 is a plot illustrating pump rate versus time, according to one ormore aspects of the present disclosure.

FIG. 6 is a plot illustrating density of the formation fluid versusexponential function of time, according to one or more aspects of thepresent disclosure.

FIG. 7 is a plot illustrating density of formation fluid versus time ata constant pump rate, according to one or more aspects of the presentdisclosure.

FIG. 8 is a plot illustrating constant pump rate versus time, accordingto one or more aspects of the present disclosure.

FIG. 9 is a plot illustrating density of the formation fluid versusexponential function of time filtered at a constant rate, according toone or more aspects of the present disclosure.

DETAILED DESCRIPTION

Certain aspects and features of the present disclosure relate tocontamination estimation of reservoir samples collected via a formationtester. Some methods of contamination estimation may inherently havenumerous uncertainties. For example, the density of the formation fluidfiltrate is not known. This may lead to inaccurate contaminationestimation as errors may exceed 100% in contamination estimation. Also,the fitting of the trend curve may be user dependent which results in avariety of contamination estimates. By accurately estimatingcontamination, costs of a given operation may be reduced and resourcesat a well or job site may be conserved.

These illustrative examples are given to introduce the reader to thegeneral subject matter discussed here and are not intended to limit thescope of the disclosed concepts. The following sections describe variousadditional features and examples with reference to the drawings in whichlike numerals indicate like elements, and directional descriptions areused to describe the illustrative aspects but, like the illustrativeaspects, should not be used to limit the present disclosure.

For purposes of this disclosure, an information handling system mayinclude any instrumentality or aggregate of instrumentalities operableto compute, classify, process, transmit, receive, retrieve, originate,switch, store, display, manifest, detect, record, reproduce, handle, orutilize any form of information, intelligence, or data for business,scientific, control, or other purposes. For example, an informationhandling system may be a personal computer, a network storage device, orany other suitable device and may vary in size, shape, performance,functionality, and price. The information handling system may includerandom access memory (RAM), one or more processing resources such as acentral processing unit (CPU) or hardware or software control logic,ROM, and/or other types of nonvolatile memory. Additional components ofthe information handling system may include one or more disk drives, oneor more network ports for communication with external devices as well asvarious input and output (I/O) devices, such as a keyboard, a mouse, anda video display. The information handling system may also include one ormore buses operable to transmit communications between the varioushardware components. The information handling system may also includeone or more interface units capable of transmitting one or more signalsto a controller, actuator, or like device.

For the purposes of this disclosure, a non-transitory computer-readablemedia may include any instrumentality or aggregation ofinstrumentalities that may retain data and/or instructions for a periodof time. Computer-readable media may include, for example, withoutlimitation, storage media such as a direct access storage device (forexample, a hard disk drive or floppy disk drive), a sequential accessstorage device (for example, a tape disk drive), compact disk, CD-ROM,DVD, RAM, ROM, electrically erasable programmable read-only memory(EEPROM), and/or flash memory; as well as communications media suchwires, optical fibers, microwaves, radio waves, and otherelectromagnetic and/or optical carriers; and/or any combination of theforegoing.

FIG. 1 is a schematic diagram of an apparatus 10 for transferring orretrieving material in a wellbore 30. Generally, apparatus 10illustrates a system for transferring material from a surface-locatedhydrocarbon well site 12 and retrieving material from a surface-locatedhydrocarbon well site 12. The well site 12 is located over a hydrocarbonbearing formation 14 comprising a hydrocarbon reservoir 52, which islocated below a ground surface 16. While well site 12 is illustrated ata ground surface 16, the present disclosure contemplates any one or moreembodiments implemented at a well site at any location, including, atsea above a subsea hydrocarbon bearing formation.

The wellbore 30 is formed through various earth strata including theformation 14. A pipe or casing 32 is insertable into the wellbore 30 andmay be cemented within the wellbore 30 by cement 34. A pumping system 42according to one or more aspects of the present disclosure is located atthe well site 12. The pumping system 42 may be configured to transfermaterial, such as reservoir, formation or production fluid, out of thewellbore 30 from, for example, reservoir 52. In one or more embodiments,a formation tester tool 50 is lowered into the wellbore 30 via aconveyance device 48. In one or more embodiments formation tester tool50 or any other downhole tool (not shown) may comprise one or moresensors 54. The one or more sensors 54 measure one or more properties ofdownhole fluid (such as formation fluid or drilling fluid) including,but not limited to, density, gas/oil ratio (GOR), condensate/gas ratio(CGR), capacitance, temperature, pressure, one or more gases (forexample, methane (C1), ethane (C2), propane (C3), butane (C4), pentane(C5)), one or more hydrocarbon molecules (for example, C6+),resistivity, dielectric, viscosity, and optical sensor data. Any one ormore sensors 54 may be sensitive to different types of downhole fluidssuch as resistivity and dielectric for water-based mud (“WBM”)contamination, and density and T1 log mean for oil-based mud (“OBM”)contamination. A formation tester tool 50 or any other downhole tool(not shown) may comprise any one or more sensors 54 sensitive to any oneor more different types of downhole fluids.

Conveyance device 48 may comprise a wireline, slickline, coiled tubing,jointed tubing or any other conveyance device or combination thereof.Formation tester tool 50 may collect one or more formation fluid samplesfrom wellbore 30. In one or more embodiments, any one or more formationfluid samples collected may be analyzed by control system 44 utilizingany one or more embodiments or aspects of the present disclosure. In oneor more embodiments, control system 44 may be located at the well site12 (as illustrated) or remote from the well site 12. In one or moreembodiments, control system 44 may comprise one or more informationhandling systems comprising one or more programs or instructions, suchas the information handling system 200 described with respect to FIG. 2.In one or more embodiments, control system 44 controls the operation offormation tester tool 50 and may process data received from theformation tester tool 50.

FIG. 2 is a diagram illustrating an example information handling system200, according to one or more aspects of the present disclosure. Thecontrol system 44 may take a form similar to the information handlingsystem 200 or include one or more components of information handlingsystem 200. A processor or central processing unit (CPU) 201 of theinformation handling system 200 is communicatively coupled to a memorycontroller hub (MCH) or north bridge 202. The processor 201 may include,for example a microprocessor, microcontroller, digital signal processor(DSP), application specific integrated circuit (ASIC), or any otherdigital or analog circuitry configured to interpret and/or executeprogram instructions and/or process data. Processor 201 may beconfigured to interpret and/or execute program instructions or otherdata retrieved and stored in any memory such as memory 203 or hard drive207. Program instructions or other data may constitute portions of asoftware or application for carrying out one or more methods describedherein. Memory 203 may include read-only memory (ROM), random accessmemory (RAM), solid state memory, or disk-based memory. Each memorymodule may include any system, device or apparatus configured to retainprogram instructions and/or data for a period of time (for example,computer-readable non-transitory media). For example, instructions froma software or application may be retrieved and stored in memory 203, forexample, a non-transitory memory, for execution by processor 201.

Modifications, additions, or omissions may be made to FIG. 2 withoutdeparting from the scope of the present disclosure. For example, FIG. 2shows a particular configuration of components of information handlingsystem 200. However, any suitable configurations of components may beused. For example, components of information handling system 200 may beimplemented either as physical or logical components. Furthermore, insome embodiments, functionality associated with components ofinformation handling system 200 may be implemented in special purposecircuits or components. In other embodiments, functionality associatedwith components of information handling system 200 may be implemented inconfigurable general purpose circuit or components. For example,components of information handling system 200 may be implemented byconfigured computer program instructions.

Memory controller hub 202 may include a memory controller for directinginformation to or from various system memory components within theinformation handling system 200, such as memory 203, storage element206, and hard drive 207. The memory controller hub 202 may be coupled tomemory 203 and a graphics processing unit (GPU) 204. Memory controllerhub 202 may also be coupled to an I/O controller hub (ICH) or southbridge 205. I/O controller hub 205 is coupled to storage elements of theinformation handling system 200, including a storage element 206, whichmay comprise a flash ROM that includes a basic input/output system(BIOS) of the computer system. I/O controller hub 205 is also coupled tothe hard drive 207 of the information handling system 200. I/Ocontroller hub 205 may also be coupled to a Super I/O chip 208, which isitself coupled to several of the I/O ports of the computer system,including keyboard 209 and mouse 210.

In certain embodiments, the control system 44 may comprise aninformation handling system 200 with at least a processor and a memorydevice coupled to the processor that contains a set of instructions thatwhen executed cause the processor to perform certain actions. In anyembodiment, the information handling system may include a non-transitorycomputer readable medium that stores one or more instructions where theone or more instructions when executed cause the processor to performcertain actions. As used herein, an information handling system mayinclude any instrumentality or aggregate of instrumentalities operableto compute, classify, process, transmit, receive, retrieve, originate,switch, store, display, manifest, detect, record, reproduce, handle, orutilize any form of information, intelligence, or data for business,scientific, control, or other purposes. For example, an informationhandling system may be a computer terminal, a network storage device, orany other suitable device and may vary in size, shape, performance,functionality, and price. The information handling system may includerandom access memory (RAM), one or more processing resources such as acentral processing unit (CPU) or hardware or software control logic,read only memory (ROM), and/or other types of nonvolatile memory.Additional components of the information handling system may include oneor more disk drives, one or more network ports for communication withexternal devices as well as various input and output (I/O) devices, suchas a keyboard, a mouse, and a video display. The information handlingsystem may also include one or more buses operable to transmitcommunications between the various hardware components.

FIG. 3 is a flowchart of a method of contamination estimation, accordingto one or more aspects of the present disclosure. With respect to thedescription of FIG. 3, references are made to one or more elements ofFIGS. 1 and 2. During a drilling operation a portion of the drillingfluid filtrate may penetrate into the formation or the reservoir 52. Toproperly evaluate a reservoir a representative sample of reservoir fluidis required. High overburden pressure may result in an invasion ofdrilling fluid into the reservoir which displaces the reservoir fluid.Collection of a representative reservoir fluid may require an estimationof a contamination of the reservoir (for example, contamination of thepumped reservoir fluid) before sampling. For example, formation fluidmay comprise drilling fluid, reservoir fluid and any other downholematerial or fluid. The formation fluid may be pumped out until a sampleof reservoir fluid may be collected. The sample of reservoir fluid maybe collected when a contamination of the formation fluid is at or belowa threshold level.

In one or more embodiments, at step 302, a mud filtrate sample iscollected. The mud filtrate sample may be collected at any time during awell services or production operation at the well site 12. In one ormore embodiments, the mud filtrate sample is collected from a mixer,blender, container, tank or any other storage unit or dispenser of thedrilling fluid at the surface 16. For example, a filter press may beused to extract the mud filtrate sample from a collected drilling fluidsample. In one or more embodiments, the drilling fluid sample may becollected from a subsurface location and once retrieved to the surface16 the mud filtrate sample may be extracted. In one or more embodiments,an information handling system may actuate collection of the mudfiltrate sample, for example, by transmitting a signal to a device tocause the device to collect the mud filtrate sample.

At step 304, the surface density of the mud filtrate sample isdetermined. At step 306, one or more characteristics of the mud filtratesample are determined. A mud filtrate characteristic may comprise anyone or more of type of material (for example, any one or more of methaneor any other gas, bentonite, oil, one or more synthetic fluids, water,potassium formate or any other material or combination thereof),temperature, density, viscosity, thickness, toughness, slickness orlubricity, permeability, or any other property. In one or moreembodiments, the mud filtrate sample is processed by a gas chromatographto determine a chemical composition of the mud filtrate sample.

At step 308, the downhole density of the mud filtrate sample isestimated based, at least in part, on the one or more characteristics ofthe mud filtrate sample determined at step 306 and the surface densityof the mud filtrate sample determined at step 304. In one or moreembodiments, equation of state modeling may be used to identify at acertain pressure and temperature how the mud filtrate will behavedownhole, for example, the estimated density of the mud filtratedownhole. In one or more embodiments, correlation of the one or morecharacteristics may be used to estimate a downhole mud filtrate densityfor a given temperature and pressure downhole.

At step 310, a downhole tool, such as a formation tester tool 50, ispositioned or disposed in wellbore 30. At step 312, density of theformation fluid is determined. For example, density of the formationfluid may be determined as the formation fluid is pumped from thewellbore 30. In one or more embodiments, the formation fluid density maybe determined continuously, at any predetermined time interval, inreal-time, or at any other time interval as the formation fluid ispumped from the wellbore 30. The formation fluid may comprise reservoirfluid (such as hydrocarbons), mud filtrate, water, any other type offormation fluid or material, or any combination thereof. The density ofthe formation fluid may be determined based on one or more densitymeasurements from one or more sensors 54. In one or more embodiments, acontrol system 44 retrieves the one or more density measurements fromthe one or more sensors 54, the formation tester tool 50, any otherdownhole tool, or any combination thereof and determines the density ofthe formation fluid for a given instance of time.

At step 314, an analysis may be performed or a plot may be generated ofthe formation fluid density measurements versus exponential time. Forexample, as illustrated in FIG. 4, density of the formation fluid isplotted for intervals of time according to one or more aspects of thepresent disclosure. The y-axis or vertical axis (labeled “FluidDensity”) indicates the determined density of the formation fluid ingrams per cubic centimeter (g/cc) and the x-axis or horizontal axis(labeled “time (hr)”) indicates time associated with the determineddensity in hours ranging from 0 to 2.6 hours.

At step 316, a constant pump rate is determined. A correlation may bemade between the determined pump rate at each interval of time todetermine the constant pumping rate for a given operation, snapshot oftime or predetermined timed interval. The pumping rate may be determinedusing any one or more formulas or methods known in the field of art oras provided in material or instructions associated with a given pump.FIG. 5 illustrates a plot of determined pumping rates versus timeaccording to one or more aspects of the present disclosure. The y-axisor vertical axis (labeled “Pump Rate”) indicates the rate of pumping ofthe formation fluid from the wellbore 30 cubic centimeters per second(cc/s) and the x-axis or horizontal axis (labeled “time (hr)”) indicatestime in hours associated with the determined pump rate ranging from 0 to2.6 hours.

FIG. 6 illustrates the density of the formation fluid versus exponentialfunction of time. The y-axis or vertical axis (labeled “Fluid Density”)indicates the density of formation fluid and the x-axis or horizontalaxis (labeled “Exp Function”) indicates an exponential function of time.The constant pumping rate may be determined by determining a pumpingrate at which a plurality of pumping rates associated with apredetermined time interval are within a range of deviation of eachother. For example, as illustrated in FIG. 5, if the range of deviationis five for an interval of time of 2.5 hours then the pumping rate is arange between 1 and 45 during time at or about 0.4 hours to at or about2.5 hours. The constant pumping rate may then be determined by using anyanalysis or modeling including, but not limited to, mean, median,average or any other mathematical model, or combination thereof. Forexample, the constant pumping rate may be determined to be 42.5 when thepumping rate is within a range of 40 to 45.

At step 318, the data associated with the determined density of theformation fluid versus time (for example, data illustrated in FIG. 4) isfiltered based on the determined constant pumping rate. FIG. 7 is a plotillustrating density of formation fluid versus time at a constant pumprate, according to one or more aspects of the present disclosure. FIG. 7illustrates a range of determined density versus time from FIG. 4 at afiner scale on the y-axis so that deviations in the determined densitiesassociated with the range of time correlating to the determined constantpump rate are highlighted. FIG. 7 illustrates a trend over time asopposed to a determination of exact fluid density values. FIG. 8 is aplot illustrating constant pumping rate versus time, according to one ormore aspects of the present disclosure. FIG. 8 illustrates a range ofpumping rates versus time from FIG. 5 at a finer scale on the y-axis sothat deviations in the determined pumping rates associated with therange of time correlating to the determined constant pump rate arehighlighted. FIG. 8 illustrates a trend over time as opposed to adetermination of exact pump rate.

At step 320, a plot is generated or an analysis is performed of thedetermined plurality of fluid densities associated with the determinedconstant pumping rate versus an exponential function of timecorresponding to the constant pumping rate. Using the plot or analysisfrom step 320, at step 322 a best fit linear regression is determined.For example, FIG. 9 illustrates a plot of determined fluid densityversus the exponential function of time corresponding to the constantpumping rate. A best fit linear regression line is indicated at 910. Atstep 324, a clean fluid density is determined based, at least in part,on the determined density of the formation fluid at the constant pumpingrate for an interval of time. For example, a linear regression isextrapolated for the determined density of the formation fluid versusexponential time corresponding to the constant pumping rate. At step324, the linear regression is extrapolated, for example, for a time “t”where “t” approaches infinity. In FIG. 9, the clean fluid density wherethe exponential function of time approaches or is zero correlates to adensity of at or about 0.756.

At step 326, the contamination of the formation fluid is estimatedbased, at least in part, on the clean fluid density. In one or moreembodiments, the contamination may be estimated using a material balanceequation or ratio, such as, C=(ρ_(rt)−ρ_(cf))/(ρ_(mf)−ρ_(cf)), where Cis the estimated contamination, ρ_(rt) is the value of a current densitymeasurement, for example, a real-time formation fluid densitymeasurement, ρ_(cf) is the clean fluid density and ρ_(mf) is theestimated downhole mud filtrate density, for example, the estimateddownhole mud filtrate density from step 308. In one or more embodiments,an estimated pump-out time may be determined based, at least in part, onthe estimated contamination and a trend of the estimated contamination.

At step 328, the estimated contamination or a value based, at least inpart, on the estimated contamination is compared to a predeterminedcontamination threshold. At step 330, a reservoir fluid sample iscollected based on the comparison at step 328. For example, acontamination threshold may be indicative of or correspond to 0.20(20%), 0.05 (5%) or any other value above or below. When the estimatedcontamination is at or below the threshold, a reservoir fluid sample maybe collected.

At step 332, one or more production operations or a well servicingoperation may be altered based, at least in part, on the collectedreservoir fluid sample. In one or more embodiments, the collectedreservoir sample may be compared to another reservoir fluid sample froma nearby well to determine likelihood that the two wells are connected.In one or more embodiments, one or more of the facilities design (forexample, one or more of separate design, flow line design, injectionprogram for well for asphalt treatment, general design of the wellboresurface facilities, or any other parameter of facilities design), flowassurance or reserve estimation may be altered based, at least in part,on the collected reservoir fluid sample. In one or more embodiments,another well may be drilled based, at least in part, on the collectedreservoir fluid sample. In one or more embodiments, the reservoir fluidsample may be sent to a laboratory for further testing or evaluation. Inone or more embodiments, any one or more steps of FIG. 3 may not beimplemented or may be implemented in any order.

While downhole density is discussed, the present disclosure contemplatesthat in one or more embodiments any one or more measurements from anyone or more sensors positioned on or deployed within formation testertool 50 may be utilized to determine the contamination estimation inlieu of the downhole density.

In one or more embodiments, a method for contamination estimationcomprises collecting a mud filtrate sample, determining a surfacedensity of the mud filtrate sample, estimating downhole density of themud filtrate sample based, at least in part, on the surface density,determining a plurality of formation fluid densities for a period oftime, determining the constant pumping rate, filtering the determinedformation fluid density based, at least in part, on the constant pumpingrate, determining a clean fluid density based, at least in part, on alinear regression of the filtered determined formation fluid densityversus an exponential function of time, estimating a contaminationbased, at least in part, on the clean fluid density, comparing theestimated contamination to a contamination threshold and collecting areservoir fluid sample based, at least in part, on the comparison. Inone or more embodiments, the method of contamination estimationcomprises receiving one or more formation fluid density measurementsfrom one or more sensors, wherein the determined formation fluid densityis based on the one or more formation fluid density measurements. In oneor more embodiments, the method of contamination estimation furthercomprises positioning a formation tester tool within a wellbore, whereinthe formation tester tool collects the reservoir fluid sample. In one ormore embodiments, the method of contamination estimation furthercomprises determining one or more mud filtrate characteristics of themud filtrate sample, wherein the estimated downhole density of the mudfiltrate sample is based, at least in part, on at least one of the oneor more mud filtrate characteristics. In one or more embodiments,wherein determining the constant pumping rate comprises determining apumping rate at which a plurality of pumping rates associated with apredetermined interval of time are within a range of deviation. In oneor more embodiments, the clean fluid density is determined based, atleast in part, on extrapolation of the linear regression for a time thatapproaches infinity. In one or more embodiments, estimating thecontamination comprises calculating a first calculated density,calculating a second calculated density and dividing the firstcalculated density by the second calculated density, wherein calculatingthe first calculated density comprises subtracting from a real-timeformation fluid density measurement the clean fluid density, and whereincalculating the second calculated density comprises subtracting from theestimated mud filtrated density the clean fluid density. In one or moreembodiments, the intensifier comprises a plurality of intensifiers, andwherein distribution of the hydraulic fluid to each of the plurality ofintensifiers is based, at least in part, on a fuel map.

In one or more embodiments, non-transitory computer-readable mediumstoring one or more executable instructions that, when executed, causeone or more processors to actuate collection of a mud filtrate sample,determine a surface density of the mud filtrate sample, estimatedownhole density of the mud filtrate sample based, at least in part, onthe surface density, determine a plurality of formation fluid densitiesfor a period of time, determining a constant pumping rate, filter thedetermined formation fluid density based, at least in part, on theconstant pumping rate, determine a clean fluid density based, at leastin part, on a linear regression of the filtered determined formationfluid density versus an exponential function of time, estimate acontamination based, at least in part, on the clean fluid density,compare the estimated contamination to a contamination threshold, andcollect a reservoir fluid sample based, at least in part, on thecomparison. In one or more embodiments, the one or more executableinstructions that, when executed, further cause the one or moreprocessors to receive one or more formation fluid density measurementsfrom one or more sensors, wherein the determined formation fluid densityis based on the one or more formation fluid density measurements. In oneor more embodiments, the one or more executable instructions that, whenexecuted, further cause the one or more processors to position aformation tester tool within a wellbore, wherein the formation testertool collects the reservoir fluid sample. In one or more embodiments,the one or more executable instructions that, when executed, furthercause the one or more processors to determine one or more mud filtratecharacteristics of the mud filtrate sample, wherein the estimateddownhole density of the mud filtrate sample is based, at least in part,on at least one of the one or mud filtrate characteristics. In one ormore embodiments, determining a constant pumping rate comprisesdetermining a pumping rate at which a plurality of pumping ratesassociated with a predetermined interval of time are within a range ofdeviation. In one or more embodiments, the clean fluid density isdetermined based, at least in part, on extrapolation of the linearregression for a time that approaches infinity. In one or moreembodiments, estimating the contamination comprises calculating a firstcalculated density, calculating a second calculated density and dividingthe first calculated density by the second calculated density, whereincalculating the first calculated density comprises subtracting from areal-time formation fluid density measurement the clean fluid density,and wherein calculating the second calculated density comprisessubtracting from the estimated mud filtrated density the clean fluiddensity. In one or more embodiments, the intensifier comprises aplurality of intensifiers, and wherein distribution of the hydraulicfluid to each of the plurality of intensifiers is based, at least inpart, on a fuel map. In one or more embodiments, the intensifiercomprises a plurality of intensifiers, and wherein distribution of thehydraulic fluid to each of the plurality of intensifiers is based, atleast in part, on a fuel map.

In one or more embodiments, a system for contamination estimationcomprises a mud filtrate sample, a processor, a non-transitory memorycoupled to the processor, the non-transitory memory comprising one ormore instructions that, when executed by the processor, cause theprocessor to determine a surface density of the mud filtrate sample,estimate downhole density of the mud filtrate sample based, at least inpart, on the surface density, determine a plurality of formation fluiddensities for a period of time, determine a constant pumping rate,filter the determined formation fluid density based, at least in part,on the constant pumping rate, determine a clean fluid density based, atleast in part, on a linear regression of the filtered determinedformation fluid density versus an exponential function of time, estimatea contamination based, at least in part, on the clean fluid density,compare the estimated contamination to a contamination threshold andcollect a reservoir fluid sample based, at least in part, on thecomparison. In one or more embodiments, the one or more instructionsthat, when executed by the processor, further cause the processor toreceive one or more formation fluid density measurement from one or moresensors, wherein the determined formation fluid density is based on theone or more formation fluid density measurements. In one or moreembodiments, the one or more instructions that, when executed by theprocessor, further cause the processor to determine one or more mudfiltrate characteristics of the mud filtrate sample, wherein theestimated downhole density of the mud filtrate sample is based, at leastin part, on at least one of the one or mud filtrate characteristics. Inone or more embodiments, estimating the contamination comprisescalculating a first calculated density, calculating a second calculateddensity and dividing the first calculated density by the secondcalculated density, wherein calculating the first calculated densitycomprises subtracting from a real-time formation fluid densitymeasurement the clean fluid density, and wherein calculating the secondcalculated density comprises subtracting from the estimated mudfiltrated density the clean fluid density.

The foregoing description of certain aspects, including illustratedaspects, has been presented only for the purpose of illustration anddescription and is not intended to be exhaustive or to limit thedisclosure to the precise forms disclosed. Numerous modifications,adaptations, and uses thereof will be apparent to those skilled in theart without departing from the scope of the disclosure.

What is claimed is:
 1. A method for contamination estimation,comprising: collecting a mud filtrate sample; determining a surfacedensity of the mud filtrate sample; estimating downhole density of themud filtrate sample based, at least in part, on the surface density;determining a plurality of formation fluid densities for a period oftime; determining a constant pumping rate; filtering the determinedformation fluid density based, at least in part, on the constant pumpingrate; determining a clean fluid density based, at least in part, on alinear regression of the filtered determined formation fluid densityversus an exponential function of time; estimating a contaminationbased, at least in part, on the clean fluid density; comparing theestimated contamination to a contamination threshold; and collecting areservoir fluid sample based, at least in part, on the comparison. 2.The method of contamination estimation of claim 1, further comprisingreceiving one or more formation fluid density measurements from one ormore sensors, wherein the determined formation fluid density is based onthe one or more formation fluid density measurements.
 3. The method ofcontamination estimation of claim 1, further comprising positioning aformation tester tool within a wellbore, wherein the formation testertool collects the reservoir fluid sample.
 4. The method of contaminationestimation of claim 1, further comprising determining one or more mudfiltrate characteristics of the mud filtrate sample, wherein theestimated downhole density of the mud filtrate sample is based, at leastin part, on at least one of the one or mud filtrate characteristics. 5.The method of contamination estimation of claim 1, wherein determiningthe constant pumping rate comprises determining a pumping rate at whicha plurality of pumping rates associated with a predetermined interval oftime are within a range of deviation.
 6. The method of contaminationestimation of claim 1, wherein the clean fluid density is determinedbased, at least in part, on extrapolation of the linear regression for atime that approaches infinity.
 7. The method of contamination estimationof claim 1, wherein estimating the contamination comprises calculating afirst calculated density, calculating a second calculated density anddividing the first calculated density by the second calculated density,wherein calculating the first calculated density comprises subtractingfrom a real-time formation fluid density measurement the clean fluiddensity, and wherein calculating the second calculated density comprisessubtracting from the estimated mud filtrated density the clean fluiddensity.
 8. The method of contamination estimation of claim 1, whereinthe intensifier comprises a plurality of intensifiers, and whereindistribution of the hydraulic fluid to each of the plurality ofintensifiers is based, at least in part, on a fuel map.
 9. Anon-transitory computer-readable medium storing one or more executableinstructions that, when executed, cause one or more processors to:actuate collection of a mud filtrate sample; determine a surface densityof the mud filtrate sample; estimate downhole density of the mudfiltrate sample based, at least in part, on the surface density;determine a plurality of formation fluid densities for a period of time;determine a constant pumping rate; filter the determined formation fluiddensity based, at least in part, on the constant pumping rate; determinea clean fluid density based, at least in part, on a linear regression ofthe filtered determined formation fluid density versus an exponentialfunction of time; estimate a contamination based, at least in part, onthe clean fluid density; compare the estimated contamination to acontamination threshold; and collect a reservoir fluid sample based, atleast in part, on the comparison.
 10. The non-transitorycomputer-readable medium of claim 9, wherein the one or more executableinstructions that, when executed, further cause the one or moreprocessors to receive one or more formation fluid density measurementsfrom one or more sensors, wherein the determined formation fluid densityis based on the one or more formation fluid density measurements. 11.The non-transitory computer-readable medium of claim 9, wherein the oneor more executable instructions that, when executed, further cause theone or more processors to position a formation tester tool within awellbore, wherein the formation tester tool collects the reservoir fluidsample.
 12. The non-transitory computer-readable medium of claim 9,wherein the one or more executable instructions that, when executed,further cause the one or more processors to determine one or more mudfiltrate characteristics of the mud filtrate sample, wherein theestimated downhole density of the mud filtrate sample is based, at leastin part, on at least one of the one or mud filtrate characteristics. 13.The non-transitory computer-readable medium of claim 9, whereindetermining the constant pumping rate comprises determining a pumpingrate at which a plurality of pumping rates associated with apredetermined interval of time are within a range of deviation.
 14. Thenon-transitory computer-readable medium of claim 9, wherein the cleanfluid density is determined based, at least in part, on extrapolation ofthe linear regression for a time that approaches infinity.
 15. Thenon-transitory computer-readable medium of claim 9, wherein estimatingthe contamination comprises calculating a first calculated density,calculating a second calculated density and dividing the firstcalculated density by the second calculated density, wherein calculatingthe first calculated density comprises subtracting from a real-timeformation fluid density measurement the clean fluid density, and whereincalculating the second calculated density comprises subtracting from theestimated mud filtrated density the clean fluid density.
 16. Thenon-transitory computer-readable medium of claim 9, wherein theintensifier comprises a plurality of intensifiers, and whereindistribution of the hydraulic fluid to each of the plurality ofintensifiers is based, at least in part, on a fuel map.
 17. A system forcontamination estimation, comprising: a mud filtrate sample; aprocessor; a non-transitory memory coupled to the processor, thenon-transitory memory comprising one or more instructions that, whenexecuted by the processor, cause the processor to: determine a surfacedensity of the mud filtrate sample; estimate downhole density of the mudfiltrate sample based, at least in part, on the surface density;determine a plurality of formation fluid densities for a period of time;determine a constant pumping rate; filter the determined formation fluiddensity based, at least in part, on the constant pumping rate; determinea clean fluid density based, at least in part, on a linear regression ofthe filtered determined formation fluid density versus an exponentialfunction of time; estimate a contamination based, at least in part, onthe clean fluid density; compare the estimated contamination to acontamination threshold; and collect a reservoir fluid sample based, atleast in part, on the comparison.
 18. The system for contaminationestimation of claim 17, wherein the one or more instructions that, whenexecuted by the processor, further cause the processor to receive one ormore formation fluid density measurement from one or more sensors,wherein the determined formation fluid density is based on the one ormore formation fluid density measurements.
 19. The system forcontamination estimation of claim 17, wherein the one or moreinstructions that, when executed by the processor, further cause theprocessor to determine one or more mud filtrate characteristics of themud filtrate sample, wherein the estimated downhole density of the mudfiltrate sample is based, at least in part, on at least one of the oneor mud filtrate characteristics.
 20. The system for contaminationestimation of claim 17, wherein estimating the contamination comprisescalculating a first calculated density, calculating a second calculateddensity and dividing the first calculated density by the secondcalculated density, wherein calculating the first calculated densitycomprises subtracting from a real-time formation fluid densitymeasurement the clean fluid density, and wherein calculating the secondcalculated density comprises subtracting from the estimated mudfiltrated density the clean fluid density.